1. Field of the Invention
The present invention relates to an endmember spectrum database construction method, an endmember spectrum database construction apparatus and an endmember spectrum database construction program product used to construct an endmember spectrum database which stores endmember spectra of objects each including a plurality of substances.
2. Description of the Related Art
In recent years, a technique called hyperspectral imaging is appearing. Hereafter, such a technique will be described briefly.
A camera for hyperspectral imaging is mounted on an airplane or an earth satellite, and the image on the ground is picked up by this camera from the sky. A spectrum apparatus disposed behind a lens of this camera separates light having visible light wavelengths, near infrared ray wavelengths and far infrared ray wavelengths (for example, 300 nm to 2,000 nm) into spectral components every predetermined wavelength width. Each of a plurality of image pickup elements disposed in the camera outputs an image of each wavelength. In this way, an image can be obtained every pixel every wavelength. Such an image is a hyperspectral image.
If the image of each pixel and each wavelength is an image with light being separated into N (for example, 200) wavelength components, an N-dimensional vector including N wavelength components can be formed. Therefore, each pixel can be associated with an N-dimensional vector at that pixel.
If each pixel contains only light reflected by a single substance, a spectrum of that pixel coincides with a spectrum of the single substance. Such a spectrum of the single substance can be obtained in a laboratory and stored in a spectrum library.
However, spatial resolution of the image picked up from the sky is not so high, and the spectrum of each pixel becomes a mixture of spectra of a plurality of substances. In many cases, therefore, it is meaningless to analyze a hyperspectral image obtained from an image picked up from the sky and attempt to know a substance that has produced a pixel as a result of image pickup every pixel, except when the same substance is distributed over a wide area.
On the other hand, it is considered that a spectrum of a pixel obtained by picking up an image of a ship of a certain kind and a spectrum of a pixel obtained by picking up another ship of the same kind are common in at least a part, because there is a common substance between the ships of that kind. In the same way, it is considered that a spectrum of a pixel obtained by picking up an image of an automobile of a certain kind and a spectrum of a pixel obtained by picking up another automobile of the same kind are common in at least a part. It is also considered that a spectrum of a pixel obtained by picking up an image of a railway vehicle of a certain kind and a spectrum of a pixel obtained by picking up another railway vehicle of the same kind are common in at least a part. It is also considered that a spectrum of a pixel obtained by picking up an image of a railway vehicle of a certain kind and a spectrum of a pixel obtained by picking up another railway vehicle of the same kind are common in at least a part. It is also considered that a spectrum of a pixel obtained by picking up an image of a house of a certain kind and a spectrum of a pixel obtained by picking up another house of the same kind are common in at least a part.
If objects each including a plurality of substances are associated respectively with spectra, therefore, it is considered that the object can be guessed from its spectrum. Therefore, it is considered that it becomes possible to know which substance is distributed in what manner by picking up an image on the ground from the sky and obtaining a hyperspectral image.
Each pixel includes not only an object but also the ground serving as the background. Therefore, spectra of each pixel do not perfectly coincide with spectra of only the object. If an endmember spectrum is found from the whole image, however, it is considered that it becomes possible to associate the endmember spectrum with the object. If an N-dimensional vector obtained by dividing a spectrum of each of pixels forming an image into N bands and making the magnitude of each dimension equal to the level of each band can be expressed as a linear combination of M N-dimensional vectors with respect to pixels of a great part of the image (especially pixels crowding like a cloud in the N-dimensional space), each of the M N-dimensional vectors is referred to as endmember spectrum. Typically, a plurality of endmember spectra are distributed at ends of a region where vector tips crowd like a cloud.